An efficient learning algorithm for periodic perceptron to test XOR function and parity problem

Abstract

Artificial neural network (ANN) is an important tool, which is used in numerous fields, such as computer vision, pattern recognition, signal processing, solving optimization problems, and voice analysis and synthesis. Many real-life problems, where the future events play a vital role, are based on the past history. For example, predicting the behavior of stock market indices and electrical load forecasting. In this paper, we establish an efficient learning algorithm for periodic perceptron (PP) in order to test in realistic problems, such as the XOR function and the parity problem. Here, the periodic threshold output function guarantees the convergence of the learning algorithm for the multilayer perceptron. By using the binary Boolean function and the PP in single and multilayer perceptron, XOR problem is solved. The performance of PP is compared with multilayer perceptron and the result shows superiority of PP over the multilayer perceptron.